GPT Researcher is an open-source autonomous agent designed to automate in-depth research tasks by leveraging large language models (LLMs) and intelligent information gathering. It addresses critical limitations of standard LLMs—such as hallucination, outdated knowledge, token constraints, and biased sourcing—by systematically planning research questions, scraping diverse web sources, and synthesizing factual, well-cited reports. Built on Plan-and-Solve and RAG architectures, it enables users to produce comprehensive 2,000+ word research reports without manual effort. This tool is ideal for researchers, analysts, developers, and content creators who need accurate, up-to-date insights from multiple sources without manual aggregation.
It supports both web-based and local document research, integrates with MCP for connecting to GitHub repos and databases, and offers a multi-agent system via LangGraph for advanced reasoning. With Docker, pip, and Colab deployment options, it’s accessible to both technical users and teams seeking scalable research automation.
What You Get
- Detailed research reports with citations - Generates comprehensive reports exceeding 2,000 words, automatically sourcing and citing all referenced materials to ensure verifiability and reduce hallucination.
- Web and local document research - Aggregates data from over 20 web sources via Tavily and JavaScript-enabled scraping, while also processing local files (PDF, DOCX, CSV, Markdown, Excel, PPT) using the DOC_PATH environment variable.
- MCP integration for custom data sources - Connects to GitHub repositories, databases, and APIs via the Model Context Protocol (MCP), enabling research beyond public web content using commands like npx -y @modelcontextprotocol/server-github.
- Deep Research mode - Uses recursive, tree-like exploration to dive into subtopics with configurable depth and breadth, enabling multi-layered analysis of complex subjects in ~5 minutes per run.
- Multi-agent research with LangGraph - Leverages specialized AI agents coordinated via LangGraph to plan, retrieve, and synthesize research results with improved depth and reasoning quality.
- Multiple export formats - Exports final reports as PDF, Word (DOCX), and Markdown for easy sharing, publishing, or integration into workflows.
- JavaScript-enabled web scraping - Bypasses static HTML limitations by executing JavaScript on target pages to extract dynamic content and modern web data.
- Customizable frontend interfaces - Offers both a lightweight FastAPI HTML/CSS/JS frontend and a production-grade Next.js + Tailwind UI with real-time progress tracking and customizable settings.
Common Use Cases
- Building a market analysis report for investors - Researching trends in AI chip demand by aggregating financial news, earnings calls, and analyst reports from 20+ sources to generate a cited 5-page PDF report for stakeholder review.
- Creating technical documentation from internal knowledge bases - Using DOC_PATH to scan a company’s PDF manuals, Git commits, and Confluence pages to auto-generate an updated product feature summary without manual consolidation.
- Problem: Manual research takes days → Solution: GPT Researcher automates it in minutes - A journalist needs to verify claims about a new AI regulation; GPT Researcher pulls from government documents, academic papers, and news outlets to produce a fact-checked summary with sources.
- DevOps teams automating competitive intelligence - Teams use GPT Researcher to monitor competitor product launches by querying GitHub repos via MCP and web sources, then exporting findings into internal wikis as Markdown reports.
Under The Hood
The GPT Researcher is a Python-based research automation tool that leverages large language models (LLMs) to generate comprehensive reports from diverse data sources. It supports multiple output formats and integrates seamlessly with LangChain and LangGraph for enhanced research workflows.
Architecture
The system adopts a modular, layered architecture that separates core logic from reporting and external integrations. It includes distinct components for memory management, report generation, and server functionality.
- Clear separation of concerns between CLI, web server, and frontend modules
- Well-defined layers for data processing, LLM interaction, and result formatting
- Use of state management through LangGraph to support complex research flows
Tech Stack
The project is built primarily in Python, with extensions in TypeScript and JavaScript for frontend and Discord bot capabilities. It integrates modern AI frameworks to enable scalable research automation.
- Python as the core backend language with extensive LLM and LangChain integration
- TypeScript and JavaScript for frontend UI and Discord bot extensions
- CI/CD pipelines and linting configured to support development workflows
Code Quality
The codebase reflects a mixed level of quality with some structured testing and error handling, though consistency varies across modules. The project shows effort toward modularization but lacks full code analysis for comprehensive assessment.
- Extensive test suite covering scraping, LLM interactions, and logging behaviors
- Error handling implemented with generic try/except blocks but limited by specificity
- Inconsistent code style and naming patterns across different modules
- Signs of technical debt in configuration and module organization
What Makes It Unique
The project stands out through its hybrid approach to research automation and extensible architecture that supports multiple report types and usage patterns.
- Combines LLM-powered research with structured state management using LangGraph
- Offers flexible deployment models including CLI and API-based interfaces
- Integrates both web and local data sources for comprehensive research workflows